Breath analysis has long been recognized as a reliable technique for diagnosing certain medical conditions including tissue inflammation (e.g. asthma), immune responses (e.g. to cancer cells or bacteria), metabolic disorders (e.g. diabetes), digestive processes, liver and/or kidney disorders, gum disease, halitosis, and other physiological conditions (Buszewski et al. Biomed. Chromatogr., 2007, 21, 553-566). The diagnosis is usually performed by collecting breath samples to a container followed by subsequent measurements of specific volatile organic compounds (VOCs).
The composition of VOCs in exhaled breath is dependent upon cellular metabolic processes. In control individuals, the composition provides a distinct chemical signature with relatively narrow variability between samples from a single individual and samples from different individuals. The composition of VOCs includes saturated and unsaturated hydrocarbons, oxygen containing compounds, sulfur containing compounds, and nitrogen containing compounds.
In exhaled breath of patients with cancer, elevated levels of certain VOCs including volatile C4-C20 alkane compounds, specific monomethylated alkanes as well as benzene derivatives were found. Hence, the composition of VOCs in exhaled breath of patients with cancer differs from that of control individuals, and can therefore be used to diagnose cancer, and to monitor disease progression or therapy-mediated disease regression. An additional advantage for diagnosing cancer through breath is the non-invasiveness of the technique which holds the potential for large-scale screening.
In recent years many attempts have been made to identify one specific pattern of volatile organic compounds (VOCs) in the breath of lung cancer patients. Phillips et al. (Lancet, 1999, 353, 1930-1933) used discriminant analysis to detect a combination of 22 breath VOCs as the “fingerprint” of lung cancer. Phillips et al. (Chest, 2003, 123, 2115-2123) then used a predictive model employing 9 VOCs which was found to exhibit sufficient sensitivity and specificity to be used as screen for lung cancer. In a more recent study Phillips et al. (Cancer Biomarkers, 2007, 3, 95-109) described the use of multi-linear regression and fuzzy logic to analyze breath samples of lung cancer patients. This study provided a set of 16 VOCs as the major identifiers of primary lung cancer in breath. The use of weighted digital analysis to select 30 breath VOCs as candidate biomarkers of primary lung cancer was then employed (Phillips et al. Clinica Chimica Acta, 2008, 393, 76-84).
Yu et al. (Sensors, Proceedings of IEEE, 2003, 2, 1333-1337) used an electronic nose device with capillary column GC and a pair of surface acoustic wave sensors to detect 9 VOCs as markers for lung cancer. Chen et al. (Meas. Sci. Technol. 2005, 16, 1535-1546) used a set of 11 VOCs to calibrate sensors array based on surface acoustic wave to diagnose lung cancer patients. In another study Chen et al. (Cancer, 2007, 110, 835-844) identified 4 special VOCs that were found to exist in all culture mediums of lung cancer cells and can be used as markers of lung cancer. Di Natale et al. (Biosensors and Bioelectronics, 2003, 18, 1209-1218) used an array of non-selective gas sensors for detecting various alkanes and benzene derivatives as possible candidate markers of lung cancer. Gordon et al. (Clin. Chem., 1985, 31(8), 1278-1282) used breath collection technique and computer-assisted gas chromatography/mass spectrometry to identify several volatile organic compounds in the exhaled breath of lung cancer patients which appear to be associated with the disease. Song et al. (Lung Cancer, 2009, 67, 227-231) reported that 1-butanol and 3-hydroxy-2-butanone were found at significantly higher concentrations in the breath of the lung cancer patients compared to the controls. These two VOCs are thus potential biomarkers useful for diagnosing lung cancer. O'neill et al. (Clinical Chemistry, 1988, 34(8), 1613-1617) reported a list of 28 VOCs found in over 90% occurrence in expired-air samples from lung cancer patients. Wehinger et al. (Inter. J. Mass Spectrometry, 2007, 265, 49-59) used proton transfer reaction mass-spectrometric analysis to detect lung cancer in human breath. Two VOCs were found to best discriminate between exhaled breath of primary lung cancer cases and control. Gaspar et al. (J. Chromatography A, 2009, 1216, 2749-2756) used linear and branched C14-C24 hydrocarbons from exhaled air of lung cancer patients, smokers and non-smokers for multivariable analysis to identify biomarkers in lung disorders. Poli et al. (Respiratory Research, 2005, 6, 71-81) showed that the combination of 13 VOCs allowed the correct classification of cases into groups of smokers, patients with chronic obstructive pulmonary disease, patients with non-small cells lung cancer and controls. Recently Poli et al. (Acta Biomed, 2008, 79(1), 64-72) measured VOC levels in exhaled breath of operated lung cancer patients, one months and three years after surgical removal of the tumor. Peng et al. (Nature Nanotech, 2009, 4, 669-673) identified 42 VOCs that represent lung cancer biomarkers using gas chromatography/mass spectrometry.
In addition to the many studies that were aimed at identifying VOCs indicative of lung cancer from breath samples Filipiak et al. (Cancer Cell International, 2008, 8, 17) disclosed a list of 60 substances observed in the headspace of medium as well as in the headspace of lung cancer cell line CALU-1. A significant increase in the concentrations of 4 VOCs and a decrease in the concentrations of 11 VOCs as compared to medium controls were detected after 18 hours. These studies cumulatively provided over 150 VOCs as potential lung cancer biomarkers in breath samples.
WO 2000/041623 to Phillips discloses a process for determining the presence or absence of a disease, particularly breast or lung cancer, in a mammal, comprising collecting a representative sample of alveolar breath and a representative sample of ambient air, analyzing the samples of breath and air to determine content of n-alkanes having 2 to 20 carbon atoms, inclusive, calculating the alveolar gradients of the n-alkanes in the breath sample in order to determine the alkane profile, and comparing the alkane profile to baseline alkane profiles calculated for mammals known to be free of the disease to be determined, wherein finding of differences in the alkane profile from the baseline alkane profile being indicative of the presence of the disease.
There is an unmet need for a set of volatile organic compounds which provides improved diagnosis of lung cancer. Furthermore, there is an unmet need for the identification of a unique signature of lung cancer in breath samples to enable non-invasive large scale screening.